An Improved Smooth Variable Structure Filter for Robust Target Tracking

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation perfor...

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Autores principales:	Yu Chen, Luping Xu, Guangmin Wang, Bo Yan, Jingrong Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	state estimation target tracking smooth variable structure filter Kalman filter Science Q
Acceso en línea:	https://doaj.org/article/ad3f1de76563479b9a0d83323e2896fb
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id	oai:doaj.org-article:ad3f1de76563479b9a0d83323e2896fb
record_format	dspace
spelling	oai:doaj.org-article:ad3f1de76563479b9a0d83323e2896fb2021-11-25T18:54:43ZAn Improved Smooth Variable Structure Filter for Robust Target Tracking10.3390/rs132246122072-4292https://doaj.org/article/ad3f1de76563479b9a0d83323e2896fb2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4612https://doaj.org/toc/2072-4292As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.Yu ChenLuping XuGuangmin WangBo YanJingrong SunMDPI AGarticlestate estimationtarget trackingsmooth variable structure filterKalman filterScienceQENRemote Sensing, Vol 13, Iss 4612, p 4612 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	state estimation target tracking smooth variable structure filter Kalman filter Science Q
spellingShingle	state estimation target tracking smooth variable structure filter Kalman filter Science Q Yu Chen Luping Xu Guangmin Wang Bo Yan Jingrong Sun An Improved Smooth Variable Structure Filter for Robust Target Tracking
description	As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.
format	article
author	Yu Chen Luping Xu Guangmin Wang Bo Yan Jingrong Sun
author_facet	Yu Chen Luping Xu Guangmin Wang Bo Yan Jingrong Sun
author_sort	Yu Chen
title	An Improved Smooth Variable Structure Filter for Robust Target Tracking
title_short	An Improved Smooth Variable Structure Filter for Robust Target Tracking
title_full	An Improved Smooth Variable Structure Filter for Robust Target Tracking
title_fullStr	An Improved Smooth Variable Structure Filter for Robust Target Tracking
title_full_unstemmed	An Improved Smooth Variable Structure Filter for Robust Target Tracking
title_sort	improved smooth variable structure filter for robust target tracking
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ad3f1de76563479b9a0d83323e2896fb
work_keys_str_mv	AT yuchen animprovedsmoothvariablestructurefilterforrobusttargettracking AT lupingxu animprovedsmoothvariablestructurefilterforrobusttargettracking AT guangminwang animprovedsmoothvariablestructurefilterforrobusttargettracking AT boyan animprovedsmoothvariablestructurefilterforrobusttargettracking AT jingrongsun animprovedsmoothvariablestructurefilterforrobusttargettracking AT yuchen improvedsmoothvariablestructurefilterforrobusttargettracking AT lupingxu improvedsmoothvariablestructurefilterforrobusttargettracking AT guangminwang improvedsmoothvariablestructurefilterforrobusttargettracking AT boyan improvedsmoothvariablestructurefilterforrobusttargettracking AT jingrongsun improvedsmoothvariablestructurefilterforrobusttargettracking
_version_	1718410546017992704

An Improved Smooth Variable Structure Filter for Robust Target Tracking

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